SQL JOIN 连接
SQL 连接(JOIN) 子句用于将数据库中两个或者两个以上表中的记录组合起来。连接通过共有值将不同表中的字段组合在一起。
我们来看看"Orders"表中的选择:
OrderID | CustomerID | OrderDate |
---|---|---|
10308 | 2 | 1996-09-18 |
10309 | 37 | 1996-09-19 |
10310 | 77 | 1996-09-20 |
然后,查看"Customers"表中的选择:
CustomerID | CustomerName | ContactName | Country |
---|---|---|---|
1 | Alfreds Futterkiste | Maria Anders | Germany |
2 | Ana Trujillo Emparedados y helados | Ana Trujillo | Mexico |
3 | Antonio Moreno Taquería | Antonio Moreno | Mexico |
请注意,"Orders"表中的"客户ID"列是指"CustomerID"表中的"客户ID"。上面两个表格之间的关系是"CustomerID"列。
然后,我们可以创建下面的SQL语句(包含一个INNER JOIN),它选择两个表中具有匹配值的记录:
** 代码示例:**
SELECT Orders.OrderID, Customers.CustomerName, Orders.OrderDate
FROM Orders
INNER JOIN Customers ON Orders.CustomerID=Customers.CustomerID;
它会产生这样的东西:
OrderID | CustomerName | OrderDate |
---|---|---|
10308 | Ana Trujillo Emparedados y helados | 9/18/1996 |
10365 | Antonio Moreno Taquería | 11/27/1996 |
10383 | Around the Horn | 12/16/1996 |
10355 | Around the Horn | 11/15/1996 |
10278 | Berglunds snabbköp | 8/12/1996 |
考虑下面两个表,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)另一个表是 ORDERS 表:
+-----+---------------------+-------------+--------+
|OID | DATE | CUSTOMER_ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在,让我们用 SELECT 语句将这个两张表连接(JOIN)在一起:
SQL> SELECT ID, NAME, AGE, AMOUNT
FROM CUSTOMERS, ORDERS
WHERE CUSTOMERS.ID = ORDERS.CUSTOMER_ID;
上述语句的运行结果如下所示:
+----+----------+-----+--------+
| ID | NAME | AGE | AMOUNT | +----+----------+-----+--------+ | 3 | kaushik | 23 | 3000 |
| 3 | kaushik | 23 | 1500 | | 2 | Khilan | 25 | 1560 |
| 4 | Chaitali | 25 | 2060 | +----+----------+-----+--------+
不同类型的SQL联接
SQL 中有多种不同的连接:
- 内连接(INNER JOIN):当两个表中都存在匹配时,才返回行。
- 左连接(LEFT JOIN):返回左表中的所有行,即使右表中没有匹配的行。
- 右连接(RIGHT JOIN):返回右表中的所有行,即使左表中没有匹配的行。
- 全连接(FULL JOIN):只要某一个表存在匹配,就返回行。
- 笛卡尔连接(CARTESIAN JOIN):返回两个或者更多的表中记录集的笛卡尔积。
内连接
最常用也最重要的连接形式是内连接,有时候也被称作"EQUIJOIN"(等值连接)。
内连接根据连接谓词来组合两个表中的字段,以创建一个新的结果表。SQL 查询会比较逐个比较表 1 和表 2 中的每一条记录,来寻找满足连接谓词的所有记录对。当连接谓词得以满足时,所有满足条件的记录对的字段将会结合在一起构成结果表。
语法:
** 内连接**的基本语法如下所示:
SELECT table1.column1, table2.column2...
FROM table1
INNER JOIN table2
ON table1.common_field = table2.common_field;
示例:
考虑如下两个表格,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)ORDERS 表:
+-----+---------------------+-------------+--------+
| OID | DATE | ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在,让我们用内连接将这两个表连接在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
INNER JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID;
上述语句将会产生如下结果:
+----+----------+--------+---------------------+
| ID | NAME | AMOUNT | DATE | +----+----------+--------+---------------------+ | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 2 | Khilan | 1560 | 2009-11-20 00:00:00 |
| 4 | Chaitali | 2060 | 2008-05-20 00:00:00 | +----+----------+--------+---------------------+
左连接
** 左链接**返回左表中的所有记录,即使右表中没有任何满足匹配条件的记录。这意味着,如果 ON 子句在右表中匹配到了 0 条记录,该连接仍然会返回至少一条记录,不过返回的记录中所有来自右表的字段都为 NULL。
这就意味着,左连接会返回左表中的所有记录,加上右表中匹配到的记录,或者是 NULL (如果连接谓词无法匹配到任何记录的话)。
语法:
** 左连接**的基本语法如下所示:
SELECT table1.column1, table2.column2...
FROM table1
LEFT JOIN table2
ON table1.common_field = table2.common_field;
这里,给出的条件可以是任何根据你的需要写出的条件。
示例:
考虑如下两个表格,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)ORDERS 表:
+-----+---------------------+-------------+--------+
| OID | DATE | ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在,让我们用左连接将这两个表连接在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
LEFT JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID;
上述语句将会产生如下结果:
+----+----------+--------+---------------------+
| ID | NAME | AMOUNT | DATE | +----+----------+--------+---------------------+ | 1 | Ramesh | NULL | NULL |
| 2 | Khilan | 1560 | 2009-11-20 00:00:00 | | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 4 | Chaitali | 2060 | 2008-05-20 00:00:00 |
| 5 | Hardik | NULL | NULL | | 6 | Komal | NULL | NULL |
| 7 | Muffy | NULL | NULL | +----+----------+--------+---------------------+
右连接
** 右链接**返回右表中的所有记录,即是左表中没有任何满足匹配条件的记录。这意味着,如果 ON 子句在左表中匹配到了 0 条记录,该连接仍然会返回至少一条记录,不过返回的记录中所有来自左表的字段都为 NULL。
这就意味着,右连接会返回右表中的所有记录,加上左表中匹配到的记录,或者是 NULL (如果连接谓词无法匹配到任何记录的话)。
语法:
** 右连接**的基本语法如下所示:
SELECT table1.column1, table2.column2...
FROM table1
RIGHT JOIN table2
ON table1.common_field = table2.common_field;
这里,给出的条件可以是任何根据你的需要写出的条件。
示例:
考虑如下两个表格,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)ORDERS 表:
+-----+---------------------+-------------+--------+
| OID | DATE | ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在,让我们用右连接将这两个表连接在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
RIGHT JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID;
上述语句将会产生如下结果:
+------+----------+--------+---------------------+
| ID | NAME | AMOUNT | DATE | +------+----------+--------+---------------------+ | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 2 | Khilan | 1560 | 2009-11-20 00:00:00 |
| 4 | Chaitali | 2060 | 2008-05-20 00:00:00 | +------+----------+--------+---------------------+
全连接
** 全连接**将左连接和右连接的结果组合在一起。
语法:
** 全连接**的基本语法如下所示:
SELECT table1.column1, table2.column2...
FROM table1
FULL JOIN table2
ON table1.common_field = table2.common_field;
这里,给出的条件可以是任何根据你的需要写出的条件。
示例:
考虑如下两个表格,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)ORDERS 表:
+-----+---------------------+-------------+--------+
| OID | DATE | ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在让我们用全连接将两个表连接在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
FULL JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID;
上述语句将会产生如下结果:
+------+----------+--------+---------------------+
| ID | NAME | AMOUNT | DATE | +------+----------+--------+---------------------+ | 1 | Ramesh | NULL | NULL |
| 2 | Khilan | 1560 | 2009-11-20 00:00:00 | | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 4 | Chaitali | 2060 | 2008-05-20 00:00:00 |
| 5 | Hardik | NULL | NULL | | 6 | Komal | NULL | NULL |
| 7 | Muffy | NULL | NULL | | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 2 | Khilan | 1560 | 2009-11-20 00:00:00 |
| 4 | Chaitali | 2060 | 2008-05-20 00:00:00 | +------+----------+--------+---------------------+
如果你所用的数据库不支持全连接,比如 MySQL,那么你可以使用 UNION ALL子句来将左连接和右连接结果组合在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
LEFT JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID
UNION ALL
SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS
RIGHT JOIN ORDERS
ON CUSTOMERS.ID = ORDERS.CUSTOMER_ID
笛卡尔连接(交叉连接)
** 笛卡尔连接** 或者交叉连接返回两个或者更多的连接表中记录的笛卡尔乘积。也就是说,它相当于连接谓词总是为真或者缺少连接谓词的内连接。
语法:
** 笛卡尔连接** 或者说交叉连接的基本语法如下所示:
SELECT table1.column1, table2.column2...
FROM table1, table2 [, table3 ]
示例:
考虑如下两个表格,(a)CUSTOMERS 表:
+----+----------+-----+-----------+----------+
| ID | NAME | AGE | ADDRESS | SALARY |
+----+----------+-----+-----------+----------+
| 1 | Ramesh | 32 | Ahmedabad | 2000.00 |
| 2 | Khilan | 25 | Delhi | 1500.00 |
| 3 | kaushik | 23 | Kota | 2000.00 |
| 4 | Chaitali | 25 | Mumbai | 6500.00 |
| 5 | Hardik | 27 | Bhopal | 8500.00 |
| 6 | Komal | 22 | MP | 4500.00 |
| 7 | Muffy | 24 | Indore | 10000.00 |
+----+----------+-----+-----------+----------+
(b)ORDERS 表:
+-----+---------------------+-------------+--------+
| OID | DATE | ID | AMOUNT | +-----+---------------------+-------------+--------+ | 102 | 2009-10-08 00:00:00 | 3 | 3000 |
| 100 | 2009-10-08 00:00:00 | 3 | 1500 | | 101 | 2009-11-20 00:00:00 | 2 | 1560 |
| 103 | 2008-05-20 00:00:00 | 4 | 2060 | +-----+---------------------+-------------+--------+
现在,让我用内连接将这两个表连接在一起:
SQL> SELECT ID, NAME, AMOUNT, DATE
FROM CUSTOMERS, ORDERS;
上述语句将会产生如下结果:
+----+----------+--------+---------------------+
| ID | NAME | AMOUNT | DATE | +----+----------+--------+---------------------+ | 1 | Ramesh | 3000 | 2009-10-08 00:00:00 |
| 1 | Ramesh | 1500 | 2009-10-08 00:00:00 | | 1 | Ramesh | 1560 | 2009-11-20 00:00:00 |
| 1 | Ramesh | 2060 | 2008-05-20 00:00:00 | | 2 | Khilan | 3000 | 2009-10-08 00:00:00 |
| 2 | Khilan | 1500 | 2009-10-08 00:00:00 | | 2 | Khilan | 1560 | 2009-11-20 00:00:00 |
| 2 | Khilan | 2060 | 2008-05-20 00:00:00 | | 3 | kaushik | 3000 | 2009-10-08 00:00:00 |
| 3 | kaushik | 1500 | 2009-10-08 00:00:00 | | 3 | kaushik | 1560 | 2009-11-20 00:00:00 |
| 3 | kaushik | 2060 | 2008-05-20 00:00:00 | | 4 | Chaitali | 3000 | 2009-10-08 00:00:00 |
| 4 | Chaitali | 1500 | 2009-10-08 00:00:00 | | 4 | Chaitali | 1560 | 2009-11-20 00:00:00 |
| 4 | Chaitali | 2060 | 2008-05-20 00:00:00 | | 5 | Hardik | 3000 | 2009-10-08 00:00:00 |
| 5 | Hardik | 1500 | 2009-10-08 00:00:00 | | 5 | Hardik | 1560 | 2009-11-20 00:00:00 |
| 5 | Hardik | 2060 | 2008-05-20 00:00:00 | | 6 | Komal | 3000 | 2009-10-08 00:00:00 |
| 6 | Komal | 1500 | 2009-10-08 00:00:00 | | 6 | Komal | 1560 | 2009-11-20 00:00:00 |
| 6 | Komal | 2060 | 2008-05-20 00:00:00 | | 7 | Muffy | 3000 | 2009-10-08 00:00:00 |
| 7 | Muffy | 1500 | 2009-10-08 00:00:00 | | 7 | Muffy | 1560 | 2009-11-20 00:00:00 |
| 7 | Muffy | 2060 | 2008-05-20 00:00:00 | +----+----------+--------+---------------------+